Pack Size, Magic, and Rares all have the same effect on the number of maps dropped.

The double boss modifier is useless when it comes to maps.

Obtaining level 78 maps is only about the level of the map you’re running and the Rares modifier.

It is never sustainable to only run level 78 maps.

The Data

The current data set consists of 1269 runs with 1251 from Standard and 18 from Hardcore. Of those, the majority were above map level 72. A barchart of the breakdown can be found below. A total of 2243 maps dropped from all runs.

Average Drop Level
I used a normal linear regression model for the average drop level and excluded all runs without a drop because it was not informative. It looks like there may be some issues with assuming Gaussian errors but I’m not fussed enough to have to code the modeling from scratch, there’s not an easy fix for this kind of data, and the conclusions were within expectations enough that I’m comfortable drawing conclusions. The only modifier that was predictive of average drop level was the level of the map. For every increase in level, the expected average map drop level increases by 0.69. You can basically write the following for expected average map drop level by map level: At baseline (level 66), the average map drop is 66.6. Then just add .68 for each level increase. At max level (78), the average map drop is 74.82.

Skyl3lazer note: My findings were slightly different, I thought that Rares were still significant, granted only with a coefficient of about 1/5th of the Level’s, or around .136 added to the Drop Level per point of Rare Monsters

Number of Map Drops
I modeled the number of map drops using a Negative Binomial model. I tried a Poisson model but found overdispersion was an issue. Significant factors were Quantity, Pack Size, Magic, and Rares.

At baseline (no mods), the average number of map drops was 1.06.

For every x% increase in the Quantity mod, the average number of map drops increases by e(0.0052*x) times or for every unit increase in the Quantity mod, we augment the average number of map drops by 1.005. So for example, the average modifier for Quantity was 74.1 and the largest was at 144. So the average number of map drops with a 74.1 quantity (and none of the other significant mods) is 1.55 and the average number of map drops with a 144 quantity is 2.22.

For every x% increase in the Pack Size mod, the average number of map drops increases by e(0.00357*x) times or for every unit increase in the magic mod, we augment the average number of map drops by 1.004. So, for example, the smallest modifier for Pack Size was 20%, average modifier for Pack Size was 36.25% and the largest was at 50%. So the respective average number of map drops for each of those are: 1.14, 1.2, 1.26 (with no other mods).

For every x% increase in the Magic mod, the average number of map drops increases by e(0.003785*x) times or for every unit increase in the magic mod, we augment the average number of map drops by 1.004. So for example, the smallest modifier for Magic was 20%, average modifier for Magic was 36.43% and the largest was at 50%. So the respective average number of map drops for each of those are: 1.14, 1.21, 1.28 (with no other mods).

For every x% increase in the Rares mod, the average number of map drops increases by e(0.003711*x) times or for every unit increase in the magic mod, we augment the average number of map drops by 1.004. So for example, the smallest modifier for Rares was 20%, average modifier for Rares was 35.37% and the largest was at 50%. So the respective average number of map drops for each of those are: 1.14, 1.21, 1.27 (with no other mods).

It is interesting to note here that in terms of the number of map drops, the % increase modifiers (Pack Size, Rares, and Magic) have the exact same effect.

In terms of getting 78 Maps
Since it is of particular interest to find the sustainability of only running 78 Maps, I also modeled for just getting 78 maps. Since, as SunnyRay from PoE forums pointed out, only level 76+ maps can drop level 78 maps, only those were used. That left us with a good 485 observations. For this, I used a Poisson model, as there was no overdispersion issues. An ordinal logistic model was also considered since the maximum number of 78 Map drops from a single run was two, however the Poisson model performed similarly with better fit. Funny enough, although I expected level to be significant, the only other factor here that mattered were Rares.

At baseline (level 76), the average number of level 78 map drops was 0.09.

For every level increase, the average number of level 78 map drops increases by e(0.68*x) times or for every unit increase in the level of the map, we augment the average number of map drops by 1.98. So the respective average number of level 78 map drops for each eligible level are: 0.09 (level 76), 0.18 (level 77), 0.35 (level 78).

For every x% rarity increase, the average number of level 78 map drops increases by e(0.019*x) times or for every unit increase in the level of the map, we augment the average number of map drops by 1.02. So for example, the smallest modifier was 20, average modifier was 35.37 and the max modifier was 50. So the respective average number of level 78 map drops for each of those are (in a level 76 map): 0.13, 0.17, 0.23. (Just for perspective, in a level 78 map, these numbers would be: 0.51, 0.68, 0.89)

Interestingly enough, this implies that for high level maps, if you only care about getting level 78 maps, you should very much care about the Rares modifier. However, you can never expect to break even with level 78 maps and only run level 78 maps sustainably.

I’m just going to emphasize for all of these models, the rest of the mods weren’t even close to being statistically significant. Which means they are worthless for whatever we’re modeling.

Some To Do’s:

Look into transformations of data as appropriate with interactions.

Find most efficient leveling method that is also self-sustaining.

Look into why I did not find Rares to be significant for level while Sky did not. (Model level covariate on a tiered basis.)

Backwards selection was used for variable selection. All modeling was done in R.